Label-free polar metabolite quantification for untargeted metabolomics

用于非靶向代谢组学的无标记极性代谢物定量

• 批准号: 10396924
• 负责人: Thomas O Metz
• 金额: $ 21.78万
• 依托单位: BATTELLE PACIFIC NORTHWEST LABORATORIES
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10396924
• 关键词: Calibration; Clinical; Data; Development; Experimental Designs; Goals; Industrialization; Isotope Labeling; Isotopes; Knowledge; Kynurenic Acid; Label; Legal; Mainstreaming; Measures; Methods; Reproducibility; Research; Sampling; Signal Transduction; Testing; Time; United States National Institutes of Health; Work; base; case control; data sharing; improved; interest; member; metabolomics; novel

SUMMARY The primary focus of the NIH Compound Identification Development Cores (CIDC) is to use untargeted metabolomics to not only identify novel metabolites but to facilitate and improve the identification of known metabolites. Furthermore, the CIDC is mandated to promote the accuracy, reproducibility, and interlaboratory comparison of metabolomics data. One way of promoting reproducibility, improving comparability and enhancing the confidence of metabolite identification is to improve metabolite quantification -- especially for untargeted metabolomics. Indeed, as frequently shown by untargeted NMR studies, knowledge of the concentration limits of a particular metabolite can “rule-in” or “rule-out” a tentative identification. For instance, if a metabolite signal is tentatively identified as kynurenic acid, but the measured concentration is determined to be 100X times more than normal, then that tentative identification must be incorrect and thus, “ruled out”. Traditionally compound quantification in metabolomics (especially absolute quantification) has been limited to targeted metabolomics while untargeted methods have largely relied on relative quantification. Absolute quantification by LC-MS is difficult and requires isotopically labeled standards and careful calibration. Isotopic standards are expensive and difficult to obtain. As a result, the number of metabolites that can be routinely quantified by targeted LC-MS- based methods is generally less than 500. On the other hand, relative quantification is much easier and it is possible to use peak intensity comparisons between “cases” and “controls” to relatively quantify thousands of compounds with little effort. However, relative quantification has many limitations and numerous problems. In particular, relative values cannot be compared across labs, across platforms, or even over modestly separate time periods within the same lab (batch effects). This makes relative quantification fundamentally “unFAIR” from a data sharing or reproducibility perspective. Furthermore, relative quantification only works for certain limited experimental designs (cases vs. controls) and relative values can never be used in clinical, legal or industrial test settings. This limits the application of untargeted metabolomics to “research-use only”. If untargeted metabolomics is ever going to expand beyond the lab and into the mainstream, it will need to develop robust, label-free quantification methods that can work across different samples, across platforms, across labs and across time. The challenge is how to perform metabolite quantification via LC-MS without isotopic standards? Fortunately, there have been a number of recent developments and novel ideas that integrate both experimental and computation approaches that suggest it may be possible to perform accurate metabolite quantification via untargeted LC-MS metabolomics without isotopically labeled standards. Our goal is to implement, test and refine these methods, specifically for polar metabolites, and make them available to all interested CIDC members.

总结

项目成果

An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts.

• DOI: 10.1186/s13321-022-00587-7
• 发表时间: 2022-09-22
• 期刊: Journal of cheminformatics
• 影响因子: 8.6

CFM-ID 4.0: More Accurate ESI-MS/MS Spectral Prediction and Compound Identification.

• DOI: 10.1021/acs.analchem.1c01465
• 发表时间: 2021-08-31
• 期刊: ANALYTICAL CHEMISTRY
• 影响因子: 7.4
• 作者: Wang, Fei;Liigand, Jaanus;Tian, Siyang;Arndt, David;Greiner, Russell;Wishart, David S.
• 通讯作者: Wishart, David S.

Mass Spectrometry Adduct Calculator.

• DOI: 10.1021/acs.jcim.1c00579
• 发表时间: 2021-12-27
• 期刊: JOURNAL OF CHEMICAL INFORMATION AND MODELING
• 影响因子: 5.6
• 作者: Blumer, Madison R.;Chang, Christine H.;Brayfindley, Evangelina;Nunez, Jamie R.;Colby, Sean M.;Renslow, Ryan S.;Metz, Thomas O.
• 通讯作者: Metz, Thomas O.

DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data.

Deimos：用于处理高维质谱数据的开源工具。

• DOI: 10.1021/acs.analchem.1c05017
• 发表时间: 2022-04-26
• 期刊: ANALYTICAL CHEMISTRY
• 影响因子: 7.4
• 作者: Colby, Sean M.;Chang, Christine H.;Bade, Jessica L.;Nunez, Jamie R.;Blumer, Madison R.;Orton, Daniel J.;Bloodsworth, Kent J.;Nakayasu, Ernesto S.;Smith, Richard D.;Ibrahim, Yehia M.;Renslow, Ryan S.;Metz, Thomas O.
• 通讯作者: Metz, Thomas O.

CFM-ID 4.0 - a web server for accurate MS-based metabolite identification.

• DOI: 10.1093/nar/gkac383
• 发表时间: 2022-07-05
• 期刊: NUCLEIC ACIDS RESEARCH
• 影响因子: 14.9
• 作者: Wang, Fei;Allen, Dana;Tian, Siyang;Oler, Eponine;Gautam, Vasuk;Greiner, Russell;Metz, Thomas O.;Wishart, David S.
• 通讯作者: Wishart, David S.